Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm

A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods...

Full description

Bibliographic Details
Main Authors: Jihao Zhai, Junzhong Ji, Jinduo Liu
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Bioengineering
Subjects:
Online Access:https://www.mdpi.com/2306-5354/10/8/909
_version_ 1797585498960560128
author Jihao Zhai
Junzhong Ji
Jinduo Liu
author_facet Jihao Zhai
Junzhong Ji
Jinduo Liu
author_sort Jihao Zhai
collection DOAJ
description A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data.
first_indexed 2024-03-11T00:07:58Z
format Article
id doaj.art-e56127256d894eab9e99dcead8224572
institution Directory Open Access Journal
issn 2306-5354
language English
last_indexed 2024-03-11T00:07:58Z
publishDate 2023-07-01
publisher MDPI AG
record_format Article
series Bioengineering
spelling doaj.art-e56127256d894eab9e99dcead82245722023-11-19T00:17:43ZengMDPI AGBioengineering2306-53542023-07-0110890910.3390/bioengineering10080909Learning Causal Biological Networks with Parallel Ant Colony Optimization AlgorithmJihao Zhai0Junzhong Ji1Jinduo Liu2Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaBeijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaBeijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaA wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data.https://www.mdpi.com/2306-5354/10/8/909causal biological networkscausal brain networkscausal protein signaling networksparallel ant colony optimizationpheromone fusionCBNs fusion
spellingShingle Jihao Zhai
Junzhong Ji
Jinduo Liu
Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm
Bioengineering
causal biological networks
causal brain networks
causal protein signaling networks
parallel ant colony optimization
pheromone fusion
CBNs fusion
title Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm
title_full Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm
title_fullStr Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm
title_full_unstemmed Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm
title_short Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm
title_sort learning causal biological networks with parallel ant colony optimization algorithm
topic causal biological networks
causal brain networks
causal protein signaling networks
parallel ant colony optimization
pheromone fusion
CBNs fusion
url https://www.mdpi.com/2306-5354/10/8/909
work_keys_str_mv AT jihaozhai learningcausalbiologicalnetworkswithparallelantcolonyoptimizationalgorithm
AT junzhongji learningcausalbiologicalnetworkswithparallelantcolonyoptimizationalgorithm
AT jinduoliu learningcausalbiologicalnetworkswithparallelantcolonyoptimizationalgorithm